@manual{progguide:intel10,
 address = {Santa Clara, CA, USA},
 citeulike-article-id = {7544406},
 keywords = {technical\_doc},
 month = {jun},
 organization = {Intel},
 posted-at = {2010-07-27 14:35:07},
 priority = {2},
 title = {{Intel\textregistered 64 and IA-32 Architectures Software Developer's Manual Volume 3A: System Programming Guide, Part 1}},
 year = {2010}
}
        
@article{Xie:Beni:Metricclustering:XBIndex:1991,
 author = {Xie, Xuanli Lisa and Beni, Gerardo},
 title = {A Validity Measure for Fuzzy Clustering},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 issue_date = {August 1991},
 volume = {13},
 number = {8},
 month = aug,
 year = {1991},
 issn = {0162-8828},
 pages = {841--847},
 numpages = {7},
 url = {http://dx.doi.org/10.1109/34.85677},
 doi = {10.1109/34.85677},
 acmid = {117682},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {IC wafer defects, cluster centroids, color image segmentation, computer color vision system, fuzzy c-partitions, fuzzy clustering, fuzzy set theory, fuzzy validity criterion, geometric distance measure, minimisation, pattern recognition, separation index, uniqueness, validity function},
 key_old = {Xie:1991:VMF:117668.117682},
 pdf = {1991_xie_beni_metricclustering_xiebeniindex.pdf},
}

@article{Humber:Arabie:Clusteranalysis:RandIndex:1988,
 author    = {Hubert, Lawrence and Arabie, Phipps},
 title     = {Comparing partitions},
 journal   = {Journal of Classification},
 volume    = {2},
 number    = {1},
 year      = {1985},
 issn      = {0176-4268},
 doi       = {10.1007/BF01908075},
 url       = {http://dx.doi.org/10.1007/BF01908075},
 publisher = {Springer-Verlag},
 keywords  = {Measures of agreement; Measures of association; Consensus indices},
 pages     = {193-218},
 language  = {English},
 file      = {1985_humber_arabie_clusteranalysis_randindex.pdf},
}


@article{Bandyopadhyay:Maulik:GAclustering:KGA:2002,
 author = {Bandyopadhyay, S. and Maulik, U.},
 title = {An evolutionary technique based on K-means algorithm for optimal clustering in RN},
 journal = {Inf. Sci. Appl.},
 volume = {146},
 number = {1-4},
 year = {2002},
 issn = {0020-0255},
 pages = {221--237},
 doi = {http://dx.doi.org/10.1016/S0020-0255(02)00208-6},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 key_old = {634816 bandyopadhyay:maulik:02 Bandyopadhyay:Maulik:02 Bandyopadhyay:Maulik:KGAclustering:2002 Bandyopadhyay:Maulik:GAclustering:KGA:2002},
 pdf = {2002_bandyopadhyay_maulik_gaclustering_kga.pdf},
 }
 
@article{Bandyopadhyay:Maulik:GAclusteringVarK:GCUK:2002,
title = {Genetic clustering for automatic evolution of clusters and application to image classification},
journal = {Pattern Recognition},
volume = {35},
number = {6},
pages = {1197 - 1208},
year = {2002},
note = {},
issn = {0031-3203},
doi = {http://dx.doi.org/10.1016/S0031-3203(01)00108-X},
url = {http://www.sciencedirect.com/science/article/pii/S003132030100108X},
author = {Bandyopadhyay, S. and Maulik, U.},
keywords = {Clustering},
keywords = {Davies–Bouldin index},
keywords = {Genetic algorithms},
keywords = {Real encoding},
keywords = {Satellite image classification },
abstract = {In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies–Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image.},
key_old = {Bandyopadhyay20021197},
pdf = {2002_bandyopadhyay_maulik_gaclusteringvark_gcuk.pdf},
}

@article{Davies:Bouldin:Metricclustering:DB:1979,
author={Davies, David L. and Bouldin, Donald W.}, 
journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, 
title={A Cluster Separation Measure}, 
year={1979}, 
month={April}, 
volume={PAMI-1}, 
number={2}, 
pages={224-227}, 
abstract={A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.}, 
keywords={Algorithm design and analysis;Clustering algorithms;Data analysis;Density measurement;Dispersion;Humans;Missiles;Multidimensional systems;Partitioning algorithms;Performance analysis;Cluster;data partitions;multidimensional data analysis;parametric clustering;partitions;similarity measure}, 
doi={10.1109/TPAMI.1979.4766909}, 
ISSN={0162-8828},
key_old = {4766909},
pdf = {1979_davies_bouldin_metricclustering_db.pdf},
}

@article{Zhang:Cao:KernelclusteringLabelKVar:2011,
author = {Lei Zhang and Qixin Cao},
title = {A novel ant-based clustering algorithm using the kernel method },
journal = {Information Sciences },
volume = {181},
number = {20},
pages = {4658 - 4672},
year = {2011},
note = {Special Issue on Interpretable Fuzzy Systems },
issn = {0020-0255},
doi = {http://dx.doi.org/10.1016/j.ins.2010.11.005},
url = {http://www.sciencedirect.com/science/article/pii/S0020025510005505},
keywords = {Ant-based clustering},
keywords = {Kernel},
keywords = {Swarm intelligence},
keywords = {Kernel principal component analysis },
abstract = {A novel ant-based clustering algorithm integrated with the kernel (ACK) method is proposed. There are two aspects to the integration. First, kernel principal component analysis (KPCA) is applied to modify the random projection of objects when the algorithm is run initially. This projection can create rough clusters and improve the algorithm’s efficiency. Second, ant-based clustering is performed in the feature space rather than in the input space. The distance between the objects in the feature space, which is calculated by the kernel function of the object vectors in the input space, is applied as a similarity measure. The algorithm uses an ant movement model in which each object is viewed as an ant. The ant determines its movement according to the fitness of its local neighbourhood. The proposed algorithm incorporates the merits of kernel-based clustering into ant-based clustering. Comparisons with other classic algorithms using several synthetic and real datasets demonstrate that \{ACK\} method exhibits high performance in terms of efficiency and clustering quality. },
key_old = {Zhang20114658},
    pdf = {2011_zhang_cao_kernelclusteringlabelkvar.pdf},
}


@article{Dunn:ClusterMeasure:CS:1974,
    author = {Dunn, J. C.},
    citeulike-article-id = {3383091},
    journal = {Journal of Cybernetics},
    keywords = {fuzzy, validation},
    pages = {95--104},
    posted-at = {2008-10-07 14:36:28},
    priority = {2},
    title = {{Well separated clusters and optimal fuzzy-partitions}},
    volume = {4},
    year = {1974},
    key_old = {dunn74index},
    pdf = {},
}


@incollection{Sandro:Benny:IanF:ClusteringMeasure:2007,
year={2007},
isbn={978-3-540-73498-7},
booktitle={Machine Learning and Data Mining in Pattern Recognition},
volume={4571},
series={Lecture Notes in Computer Science},
editor={Perner, Petra},
doi={10.1007/978-3-540-73499-4_14},
title={A Bounded Index for Cluster Validity},
url={http://dx.doi.org/10.1007/978-3-540-73499-4_14},
publisher={Springer Berlin Heidelberg},
keywords={clustering; cluster validity; validity index; k-means},
author={Saitta, Sandro and Raphael, Benny and Smith, IanF.C.},
pages={174-187},
language={English},
key_old  = {},
pdf    = {2007_sandro_benny_ianf_clusteringmeasure.pdf},
}

@article{Calinski:Harabasz:Metricclustering:VRC:1974,
    abstract = {{A method for identifying clusters of points in a multidimensional Euclidean space is described and its application to taxonomy considered. It reconciles, in a sense, two different approaches to the investigation of the spatial relationships between the points, viz., the agglomerative and the divisive methods. A graph, the shortest dendrite of Florek etal. (1951a), is constructed on a nearest neighbour basis and then divided into clusters by applying the criterion of minimum within cluster sum of squares. This procedure ensures an effective reduction of the number of possible splits. The method may be applied to a dichotomous division, but is perfectly suitable also for a global division into any number of clusters. An informal indicator of the "best number" of clusters is suggested. It is a"variance ratio criterion" giving some insight into the structure of the points. The method is illustrated by three examples, one of which is original. The results obtained by the dendrite method are compared with those obtained by using the agglomerative method or Ward (1963) and the divisive method of Edwards and Cavalli-Sforza (1965).}},
    author = {Cali\'{n}ski, T. and Harabasz, J.},
    citeulike-article-id = {4369132},
    citeulike-linkout-0 = {http://dx.doi.org/10.1080/03610927408827101},
    citeulike-linkout-1 = {http://www.tandfonline.com/doi/abs/10.1080/03610927408827101},
    day = {1},
    doi = {10.1080/03610927408827101},
    journal = {Communications in Statistics},
    keywords = {cluster, evaluation},
    month = jan,
    number = {1},
    pages = {1--27},
    posted-at = {2010-05-22 01:44:05},
    priority = {2},
    publisher = {Taylor \& Francis},
    title = {{A dendrite method for cluster analysis}},
    url = {http://dx.doi.org/10.1080/03610927408827101},
    volume = {3},
    year = {1974},
    key_old = {citeulike:4369132},
}


@article{Maulik:Bandyopadhyay:GAclustering:IndexI:2002,
 author = {Maulik, U. and Bandyopadhyay, S.},
 title = {Performance Evaluation of Some Clustering Algorithms and Validity Indices},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 volume = {24},
 issue = {12},
 month = {December},
 year = {2002},
 issn = {0162-8828},
 pages = {1650--1654},
 numpages = {5},
 url = {http://dx.doi.org/10.1109/TPAMI.2002.1114856},
 doi = {http://dx.doi.org/10.1109/TPAMI.2002.1114856},
 acmid = {628859},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {Unsupervised classification, Euclidean distance, K-Means algorithm, single linkage algorithm, validity index, simulated annealing.},
 file  = {2002_maulik_badyopadhyay_gaclustering_indexi.pdf},
 key_old = {Maulik:2002:PEC:628333.628859},
} 

@article{Tseng:Yang:GAclusteringVarK:CLUSTERING:2001,
title = {A genetic approach to the automatic clustering problem},
author = {Lin Yu Tseng and Shiueng Bien Yang},
journal = {Pattern Recognition},
volume = {34},
number = {2},
pages = {415 - 424},
year = {2001},
note = {},
issn = {0031-3203},
doi = {http://dx.doi.org/10.1016/S0031-3203(00)00005-4},
url = {http://www.sciencedirect.com/science/article/pii/S0031320300000054},
keywords = {Clustering},
keywords = {Single-linkage algorithm},
keywords = {Genetic clustering algorithm},
abstract = {In solving the clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, clustering becomes a tedious trial-and-error work and the clustering result is often not very promising especially when the number of clusters is large and not easy to guess. In this paper, we propose a genetic algorithm for the clustering problem. This algorithm is suitable for clustering the data with compact spherical clusters. It can be used in two ways. One is the user-controlled clustering, where the user may control the result of clustering by varying the values of the parameter, w. A small value of w results in a larger number of compact clusters, while a large value of w results in a smaller number of looser clusters. The other is an automatic clustering, where a heuristic strategy is applied to find a good clustering. Experimental results are given to illustrate the effectiveness of this genetic clustering algorithm.},
file = {2001_tseng_yang_gaclusteringvark_clustering.pdf},
key_old = {Tseng200141}
}

@article{Lu:etal:GAclusteringLabel:IGKA:2004,
  author    = {Yi Lu and
               Shiyong Lu and
               Farshad Fotouhi and
               Youping Deng and
               Susan J. Brown},
  title     = {Incremental genetic K-means algorithm and its application
               in gene expression data analysis},
  journal   = {BMC Bioinformatics},
  volume    = {5},
  year      = {2004},
  pages     = {172},
  ee        = {http://dx.doi.org/10.1186/1471-2105-5-172},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  key_old   = {DBLP:journals/bmcbi/LuLFDB04},
  pdf       = {luLFDB04 luetal_gaclusteringlabel_igka_2004.pdf},
}

@inproceedings{Lu:etal:GAclusteringLabel:FGKA:2004,
 author = {Lu, Yi and Lu, Shiyong and Fotouhi, Farshad and Deng, Youping and Brown, Susan J.},
 title = {FGKA: a Fast Genetic K-means Clustering Algorithm},
 booktitle = {Proceedings of the 2004 ACM symposium on Applied computing},
 series = {SAC '04},
 year = {2004},
 isbn = {1-58113-812-1},
 location = {Nicosia, Cyprus},
 pages = {622--623},
 numpages = {2},
 url = {http://doi.acm.org/10.1145/967900.968029},
 doi = {http://doi.acm.org/10.1145/967900.968029},
 acmid = {968029},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {K-means algorithm, clustering, genetic algorithm},
 key_old = {968029 Lu:2004:FFG:967900.968029 Lu:GAClustering:FGKA:2004},
 pdf = {2004_luetal_gaclusteringlabel_fgka.pdf},
}


@article{Hruschka:Ebecken:GAClusteringLabelKVar:CGA:2003,
  author    = {Hruschka, E. R. and
               Ebecken, N. F. F.},
  title     = {A genetic algorithm for cluster analysis},
  journal   = {Intell. Data Anal.},
  year      = {2003},
  volume    = {7},
  number    = {1},
  pages     = {15--25},
  url       = {http://iospress.metapress.com/content/adhnkma5h48f1l0q/},
  timestamp = {Fri, 24 Oct 2014 10:58:50 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/ida/HruschkaE03},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  key_old   = {DBLP:journals/ida/HruschkaE03},
  file      = {},
}

@article{Hruschka:etal:GAclusteringLabelKVar:CGAII:2004,
year={2004},
isbn={978-3-540-23806-5},
booktitle={Advances in Artificial Intelligence – IBERAMIA 2004},
volume={3315},
series={Lecture Notes in Computer Science},
editor={Lemaître, Christian and Reyes, CarlosA. and González, JesúsA.},
doi={10.1007/978-3-540-30498-2_86},
title={Improving the Efficiency of a Clustering Genetic Algorithm},
url={http://dx.doi.org/10.1007/978-3-540-30498-2_86},
publisher={Springer Berlin Heidelberg},
author={Hruschka, E. R. and Campello, R. J. G. B. and de Castro, L. N.},
pages={861-870},
language={English},
key_old = {conf/iberamia/HruschkaCC04},
file = {incollection 2004_hruschka_campello_castro_gaclusteringlabelkvar_cgaii.pdf},
}

@inproceedings{Ester:Kriegel:Sander:Xu:Clustering:DBSCAN:1996},
  author = {Ester, Martin and Kriegel, Hans P. and Sander, Jorg and Xu, Xiaowei},
  title = {A {Density-Based} Algorithm for Discovering Clusters in Large Spatial Databases with Noise},
  booktitle = {Second International Conference on Knowledge Discovery and Data Mining},
  editor = {Simoudis, Evangelos and Han, Jiawei and Fayyad, Usama},
  publisher = {AAAI Press},
  year = {1996},
  address = {Portland, Oregon},
  pages = {226--231},
  citeulike-article-id = {2265233},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.2930},
  posted-at = {2010-06-08 13:17:27},
  priority = {2},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.2930},
  abstract = {Data clustering has become an important task for discovering significant patterns and characteristics in large spatial databases. The {Multi-Centroid}, {Multi-Run} Sampling Scheme ({MCMRS}) has been shown to be effective in improving the k-medoids-based clustering algorithms in our previous work. In this paper, a more advanced sampling scheme termed the Incremental ({IMCMRS}) is proposed for k-medoids-based clustering algorithms. Experimental results demonstrate the proposed scheme can not only reduce...},
  key_old = {dbscan},
  pdf = {1996_ester_kriegel_sander_xu_clustering_dbscan.pdf},  
}

@article{Bezdek:ClusterAnalysis:FCM:1974, 
title={Numerical taxonomy with fuzzy sets},
author={Bezdek, J. C.},  
volume={1}, 
url={http://www.springerlink.com/content/y6331n21l4208m8u/}, 
number={1}, 
journal={Journal of Mathematical Biology}, 
publisher={Springer}, 
year={1974}, 
pages={57--71},
key_old = {Bezdek:ClusterAnalysis:fuzzyClustering:1974},
pdf = {1973_bezdek_clustering_fuzzy_set.pdf},
}

@article{Bezdek:etal:ClusterAnalysis:FCM:1984, 
  title     = {FCM: The fuzzy c-means clustering algorithm},
  author    = {Bezdek, J. C. and Ehrlich, R. and Full, W.}, 
  volume    = {10}, 
  url       = {http://linkinghub.elsevier.com/retrieve/pii/0098300484900207}, 
  number    = {2-3}, 
  journal   = {Computers \& Geosciences}, 
  publisher = {Elsevier}, 
  year      = {1984}, 
  pages     = {191--203},
  pdf       = {1983_bezdek_clustering_fcm.pdf},
}


@article{Krishna:Murty:GAClustering:GKA:1999,
  author    = {K. Krishna and
               M. Narasimha Murty},
  title     = {Genetic K-means algorithm},
  journal   = {IEEE Transactions on Systems, Man, and Cybernetics, Part
               B},
  volume    = {29},
  number    = {3},
  year      = {1999},
  pages     = {433-439},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  key_old = {DBLP:journals/tsmc/KrishnaM99 krishna:murty:99 Krishna:Murty:GeneticClustering:GKA:1999},
  pdf       = {1999_krishna_murty_gaclustering_gka.pdf},
}

@inproceedings{MacQueen:ClusterAnalysis:KMeans:1967,
 title = {Some Methods for Classification and Analysis of Multivariate Observations},
 author = {MacQueen, J. B.},
 booktitle = {Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability},
 journal = {University of California Press},
 pages = {281-297},
 year = 1967,
 url = {http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html#macqueen},
 biburl = {http://www.bibsonomy.org/bibtex/255b2985db0b65d237559f6431dfded58/tfalk},
 keywords = {clustering},
 date = {(1967):},
 file = {macqueen67.pdf},
 key_old = {macqueen:67 Mac67},
}

@article{Murthy:Chowdhury:GAclustering:GA:1996,
 author = {Murthy, C. A. and Chowdhury, Nirmalya},
 title = {In search of optimal clusters using genetic algorithms},
 journal = {Pattern Recogn. Lett.},
 volume = {17},
 number = {8},
 year = {1996},
 issn = {0167-8655},
 pages = {825--832},
 doi = {http://dx.doi.org/10.1016/0167-8655(96)00043-8},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 key_old ={235761},
 pdf     ={1996_murthy_chowdhury_gaclustering_ga.pdf 235761.pdf},
 }

@inproceedings{Bezdek:etal:GAclustering:GA:1994,
title={Genetic algorithm guided clustering},
author={Bezdek, J. C. and Boggavarapu, S. and Hall, L. O. and Bensaid, A.},
booktitle={Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on},
year={1994},
month={Jun},
volume={},
number={},
pages={34-39 vol.1},
keywords={genetic algorithms, optimisation, pattern recognition, unsupervised learning Iris data, assigned pattern, clustering performance, feature space, fitness function, genetic algorithm guided clustering, hard c-means optimization function, hard partitions, homogeneous classes, majority class, mutation, unsupervised clustering algorithms},
doi={10.1109/ICEC.1994.350046},
ISSN={}, 
key_old = {Bezdek:etal:GAclustering:GGC:1994,  Bezdek:etal:GeneticGuidedClustering:1994 350046},
pdf = {1974_bezdek_etal_gaclustering_ggc.pdf},
}

@article{Tseng:Yang:GAclusteringVarK:CLUSTERING:2001,
title = {A genetic approach to the automatic clustering problem},
author = {Lin Yu Tseng and Shiueng Bien Yang},
journal = {Pattern Recognition},
volume = {34},
number = {2},
pages = {415 - 424},
year = {2001},
note = {},
issn = {0031-3203},
doi = {http://dx.doi.org/10.1016/S0031-3203(00)00005-4},
url = {http://www.sciencedirect.com/science/article/pii/S0031320300000054},
keywords = {Clustering},
keywords = {Single-linkage algorithm},
keywords = {Genetic clustering algorithm},
abstract = {In solving the clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, clustering becomes a tedious trial-and-error work and the clustering result is often not very promising especially when the number of clusters is large and not easy to guess. In this paper, we propose a genetic algorithm for the clustering problem. This algorithm is suitable for clustering the data with compact spherical clusters. It can be used in two ways. One is the user-controlled clustering, where the user may control the result of clustering by varying the values of the parameter, w. A small value of w results in a larger number of compact clusters, while a large value of w results in a smaller number of looser clusters. The other is an automatic clustering, where a heuristic strategy is applied to find a good clustering. Experimental results are given to illustrate the effectiveness of this genetic clustering algorithm.},
file = {2001_tseng_yang_gaclusteringvark_clustering.pdf},
key_old = {Tseng200141}
}
             
@incollection{Casillas:etal:GAclusteringVarK:GA:2003,
author={Casillas, A. and de Lena, M.T. González and Martínez, R.},
title={Document Clustering into an Unknown Number of Clusters Using a Genetic Algorithm},
year={2003},
isbn={978-3-540-20024-6},
booktitle={Text, Speech and Dialogue},
volume={2807},
series={Lecture Notes in Computer Science},
editor={Matoušek, Václav and Mautner, Pavel},
doi={10.1007/978-3-540-39398-6_7},
url={http://dx.doi.org/10.1007/978-3-540-39398-6_7},
publisher={Springer Berlin Heidelberg},
pages={43-49},
language={English},
pdf = {2003_casillas_et_al_ga_unkmown_number.pdf},
}
              
@inproceedings{Kuncheva:Bezdek:GAMedoid:GAPrototypes:1997,
 author = {L. I. Kuncheva and Bezdek, J.C.},
 title = {Selection of cluster prototypes from data by a genetic algorithm},
 booktitle = {in Proc. 5th Eur. Congr. Intell. Tech. Soft Comput.},
 year = {1997},
 volume = {},
 isbn = {},
 pages = {1683--1688},
 location = {},
 doi = {},
 publisher = {},
 address = {},
 key_old = {},
 file = {1997_kuncheva_bezdek_gaclusteringmedoid_gaprototypes.pdf},
 }


@inproceedings{Alves:etal:GAclusteringLabelKVar:FEAC:2006,
  author    = {Vinicius S. Alves and
               Ricardo J. G. B. Campello and
               Eduardo R. Hruschka},
  title     = {Towards a Fast Evolutionary Algorithm for Clustering},
  booktitle = {{IEEE} International Conference on Evolutionary Computation, {CEC}
               2006, part of {WCCI} 2006, Vancouver, BC, Canada, 16-21 July 2006},
  year      = {2006},
  pages     = {1776--1783},
  crossref  = {DBLP:conf/cec/2006},
  url       = {http://dx.doi.org/10.1109/CEC.2006.1688522},
  doi       = {10.1109/CEC.2006.1688522},
  timestamp = {Sun, 09 Nov 2014 23:33:57 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/cec/AlvesCH06},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  key_old   = {DBLP:conf/cec/AlvesCH06},
  pdf       = {2006_alves_campello_hruschka_gaclusteringlabelkvar_feac.pdf},
}

@article{Hruschka:etal:GAClusteringLabelKVar:EAC:2006,
 author = {Hruschka, E. R. and Campello, R. J. G. B. and de Castro, L. N.},
 title = {Evolving Clusters in Gene-expression Data},
 journal = {Inf. Sci.},
 issue_date = {July, 2006},
 volume = {176},
 number = {13},
 month = jul,
 year = {2006},
 issn = {0020-0255},
 pages = {1898--1927},
 numpages = {30},
 url = {http://dx.doi.org/10.1016/j.ins.2005.07.015},
 doi = {10.1016/j.ins.2005.07.015},
 acmid = {2169351},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 keywords = {Bioinformatics, Clustering, Evolutionary computation, Gene-expression data},
 key_old  = {Hruschka:2006:ECG:2169236.2169351},
 file     = {2006_hruschka_campello_castro_gaclusteringlabelkvar_eac.pdf},
} 

@article{Hruschka:etal:GAclustering:survey:2009,
author = {E.R. Hruschka and R.J.G.B. Campello and A.A. Freitas and A.C.P.L.F. de Carvalho},
title = {A survey of evolutionary algorithms for clustering},
month = {March},
year = {2009},
pages = {133-155},
keywords = {data mining, clustering, evolutionary algorithms},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2009/2884},
publication_type = {article},
submission_id = {7452_1237570382},
ISSN = {10946977},
journal = {IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews},
volume = {39},
number = {2},
publisher = {IEEE Press},
pdf = {2009_hruschka_etal_gaclustering_survey.pdf},
key_old = {hruschka:etal:09},
}


@article{He:Tan:GAclusteringVarK:TGCA:2012,
 author = {He, Hong and Tan, Yonghong},
 title = {A Two-stage Genetic Algorithm for Automatic Clustering},
 journal = {Neurocomput.},
 issue_date = {April, 2012},
 volume = {81},
 month = apr,
 year = {2012},
 issn = {0925-2312},
 pages = {49--59},
 numpages = {11},
 url = {http://dx.doi.org/10.1016/j.neucom.2011.11.001},
 doi = {10.1016/j.neucom.2011.11.001},
 acmid = {2125518},
 publisher = {Elsevier Science Publishers B. V.},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Automatic clustering, Genetic algorithm, Mutation, Two-stage selection},
 key_old  = {He:2012:TGA:2125397.2125518},
 pdf      = {2012_he_tan_gaclusteringvark_tgca.pdf},
}

@article{Franti:etal:GAclustering:gafranti:1997,
    author = {Pasi Fr\"anti and Juha Kivij\"arvi and Timo Kaukoranta and Olli Nevalainen},
    title = {Genetic Algorithms for Large Scale Clustering Problems},
    journal = {Comput. J},
    year = {1997},
    volume = {40},
    pages = {547--554},
    key_old = {Franti97geneticalgorithms},
    file = {1997_franti_etal_gaclustering.pdf},
}

@article{Chang:etal:GAclustering:GAGR:2009,
 author = {Chang, Dong-Xia and Zhang, Xian-Da and Zheng, Chang-Wen},
 title = {A genetic algorithm with gene rearrangement for K-means clustering},
 journal = {Pattern Recogn.},
 volume = {42},
 number = {7},
 year = {2009},
 issn = {0031-3203},
 pages = {1210--1222},
 doi = {http://dx.doi.org/10.1016/j.patcog.2008.11.006},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 file = {2009_chang_etal_gaclustering_gagr_2009.pdf  1518387.pdf chang:etal:09},
 key_old = {1518387 Chang:etal:GAGRclustering:2009},
}

@article{Srinivas:Patnaik:GAAdaptive:1994,
  author    = {M. Srinivas and
               Lalit M. Patnaik},
  title     = {Adaptive probabilities of crossover and mutation in genetic
               algorithms},
  journal   = {IEEE Transactions on Systems, Man, and Cybernetics},
  volume    = {24},
  number    = {4},
  year      = {1994},
  pages     = {656-667},
  ee        = {http://dx.doi.org/10.1109/21.286385},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  file      = {srinivas_patnaik_ga_adaptive_1994.pdf},
  key_old = {DBLP:journals/tsmc/SrinivasP94},
}

@article{Bandyopadhyay:Maulik:GAclusteringVarK:GCUK:2002,
title = {Genetic clustering for automatic evolution of clusters and application to image classification},
journal = {Pattern Recognition},
volume = {35},
number = {6},
pages = {1197 - 1208},
year = {2002},
note = {},
issn = {0031-3203},
doi = {http://dx.doi.org/10.1016/S0031-3203(01)00108-X},
url = {http://www.sciencedirect.com/science/article/pii/S003132030100108X},
author = {Sanghamitra Bandyopadhyay and Ujjwal Maulik},
keywords = {Clustering},
keywords = {Davies–Bouldin index},
keywords = {Genetic algorithms},
keywords = {Real encoding},
keywords = {Satellite image classification },
abstract = {In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies–Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image.},
key_old = {Bandyopadhyay20021197},
pdf = {2002_bandyopadhyay_maulik_gaclusteringvark_gcuk.pdf},
}

@article{Bandyopadhyay:Maulik:GAclusteringVarK:GCUK:2002,
title = {Genetic clustering for automatic evolution of clusters and application to image classification},
journal = {Pattern Recognition},
volume = {35},
number = {6},
pages = {1197 - 1208},
year = {2002},
note = {},
issn = {0031-3203},
doi = {http://dx.doi.org/10.1016/S0031-3203(01)00108-X},
url = {http://www.sciencedirect.com/science/article/pii/S003132030100108X},
author = {Sanghamitra Bandyopadhyay and Ujjwal Maulik},
keywords = {Clustering},
keywords = {Davies–Bouldin index},
keywords = {Genetic algorithms},
keywords = {Real encoding},
keywords = {Satellite image classification },
abstract = {In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies–Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image.},
key_old = {Bandyopadhyay20021197},
pdf = {2002_bandyopadhyay_maulik_gaclusteringvark_gcuk.pdf},
}


@article{Agustin:etal:GAclusteringVarK:GGA:2012,
 author = {Agust\'{i}n-Blas, L.E. and
           Salcedo-Sanz, S. and
	   Jim{\'e}nez-Fern\'{a}ndez, S. and
	   Carro-Calvo, L. and
	   Del Ser, J. and
	   Portilla-Figueras, J.A.},
 title = {A New Grouping Genetic Algorithm for Clustering Problems},
 journal = {Expert Syst. Appl.},
 issue_date = {August, 2012},
 volume = {39},
 number = {10},
 month = aug,
 year = {2012},
 issn = {0957-4174},
 pages = {9695--9703},
 numpages = {9},
 url = {http://dx.doi.org/10.1016/j.eswa.2012.02.149},
 doi = {10.1016/j.eswa.2012.02.149},
 acmid = {2181489},
 publisher = {Pergamon Press, Inc.},
 address = {Tarrytown, NY, USA},
 keywords = {Clustering problems, Grouping genetic algorithms, Hybrid algorithms},
 key_old  = {Agustin-Blas:2012:NGG:2181256.2181489},
 pdf      = {2012_agustin_salcedo_jimenez_carro_Ser_gaclusteringvark_gga.pdf},
}


@article{Hruschka:Ebecken:GAClusteringLabelKVar:CGA:2003,
  author    = {Eduardo R. Hruschka and
               Nelson F. F. Ebecken},
  title     = {A genetic algorithm for cluster analysis},
  journal   = {Intell. Data Anal.},
  year      = {2003},
  volume    = {7},
  number    = {1},
  pages     = {15--25},
  url       = {http://iospress.metapress.com/content/adhnkma5h48f1l0q/},
  timestamp = {Fri, 24 Oct 2014 10:58:50 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/ida/HruschkaE03},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  key_old   = {DBLP:journals/ida/HruschkaE03},
  file      = {},
}

@article{Maulik:Bandyopadhyay:GAclustering:GAS:2000,
  author    = {Ujjwal Maulik and
               Sanghamitra Bandyopadhyay},
  title     = {Genetic algorithm-based clustering technique},
  journal   = {Pattern Recognition},
  volume    = {33},
  number    = {9},
  year      = {2000},
  pages     = {1455-1465},
  ee        = {http://dx.doi.org/10.1016/S0031-3203(99)00137-5},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  key_ref = {DBLP:journals/pr/MaulikB00},
  pdf = {2000_maulik_bandyopadhyay_gaclustering_gas.pdf maulikB00.pdf},
}

@article{Bandyopadhyay:Maulik:GACVarK:GCUK:2002,
title = {Genetic clustering for automatic evolution of clusters and application to image classification},
journal = {Pattern Recognition},
volume = {35},
number = {6},
pages = {1197 - 1208},
year = {2002},
note = {},
issn = {0031-3203},
doi = {http://dx.doi.org/10.1016/S0031-3203(01)00108-X},
url = {http://www.sciencedirect.com/science/article/pii/S003132030100108X},
author = {Sanghamitra Bandyopadhyay and Ujjwal Maulik},
keywords = {Clustering},
keywords = {Davies–Bouldin index},
keywords = {Genetic algorithms},
keywords = {Real encoding},
keywords = {Satellite image classification },
abstract = {In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies–Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image.},
key_old = {Bandyopadhyay20021197},
pdf = {2002_bandyopadhyay_maulik_gaclusteringvark_gcuk.pdf},
}
          
@inproceedings{Sheng:Xiaohui:GAclusteringMedoid:HKA:2004,
title={A hybrid algorithm for k-medoid clustering of large data sets},
author={Weiguo Sheng and Xiaohui Liu},
booktitle={Evolutionary Computation, 2004. CEC2004. Congress on},
year={2004},
month={June},
volume={1},
number={},
pages={ 77-82 Vol.1},
keywords={ biology computing, computational complexity, data structures, genetic algorithms, pattern clustering, search problems, very large databases GCA, NP-hard optimization problem, data clustering, gene expression data sets, genetic algorithm, hybrid K-medoid algorithm, hybrid algorithm, k-medoid clustering, large data sets, local optimality, local search heuristic, total dissimilarity minimization, RARwGA},
doi={10.1109/CEC.2004.1330840},
ISSN={ }, 
file    = {2004_sheng_xiaohui_gaclusteringmedoid_hka.pdf 1330840.pdf},
key_old = {1330840},
}

@article{Lucasius:etal:GAclusteringMedoid:GCA:1993,
    author      = {C.B. Lucasius and A.D. Dane and G. Kateman},
    title       = {On K-medoid clustering of large data sets with the
                   aid of a genetic algorithm: background, feasibility
                   and comparison},
    journal     = {Analytica Chimica Acta},
    volume      = {282},
    number      = {},
    pages       = {647-669},
    year        = {1993},
    pdf         = {1993_lucasius_etal_gaclusteringmedoid_gca.pdf lucasius93.pdf},
    key_old     = {Lucasius93},
}


@book{Manning:etal:informationretrieval:book:2008,
  abstract = {Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective.},
  added-at = {2010-12-20T06:06:48.000+0100},
  address = {Cambridge, UK},
  author = {Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich},
  biburl = {https://www.bibsonomy.org/bibtex/29f4ab13e07b48b9723113aa74224be65/folke},
  interhash = {2e574e46b7668a7268e7f02b46f4d9bb},
  intrahash = {9f4ab13e07b48b9723113aa74224be65},
  isbn = {978-0-521-86571-5},
  keywords = {book information introduction ir retrieval},
  publisher = {Cambridge University Press},
  timestamp = {2010-12-20T06:06:48.000+0100},
  title = {Introduction to Information Retrieval},
  url = {http://nlp.stanford.edu/IR-book/information-retrieval-book.html},
  year = {2008},
  key_old = {manning2008introduction},
  pdf = {2008_manning_etal_book_informationintroductionretrieva.pdf},
}

@article{Chou:Su:Lai:ClusterMeasure:CS:2004,
author={Chou, C.-H. and Su, M.-C. and Lai, E.},
title={A new cluster validity measure and its application to image compression},
year={2004},
issn={1433-7541},
journal={Pattern Analysis and Applications},
volume={7},
number={2},
doi={10.1007/s10044-004-0218-1},
url={http://dx.doi.org/10.1007/s10044-004-0218-1},
publisher={Springer-Verlag},
keywords={Clustering algorithm; Cluster validity; Image compression; Pattern recognition; Vector quantisation},
pages={205-220},
language={English},
key_old = {},
 pdf = {2004_chou_su_lai_cluster_measure_cs.pdf},
}

@ARTICLE{Das:Abraham:Konar:GAclusteringLabelKVar:ACDE:2008,
author={Das, S. and Abraham, A. and Konar, A.},
journal={Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on},
title={Automatic Clustering Using an Improved Differential Evolution Algorithm},
year={2008},
month={Jan},
volume={38},
number={1},
pages={218-237},
abstract={Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data "on the run." Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful well-known optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting real-world application of the proposed method to automatic segmentation of images is also reported.},
keywords={genetic algorithms;image classification;image segmentation;particle swarm optimisation;pattern clustering;search problems;automatic image segmentation;data classification;differential evolution algorithm;genetic algorithm;global search heuristics;hierarchical clustering algorithm;optimization algorithms;particle swarm optimization;partitional clustering techniques;unlabeled data sets;Clustering algorithms;Councils;Evolution (biology);Genetic algorithms;Image analysis;Image segmentation;Particle swarm optimization;Partitioning algorithms;Pattern analysis;Robustness;Differential evolution (DE);genetic algorithms (GAs);particle swarm optimization (PSO);partitional clustering},
doi={10.1109/TSMCA.2007.909595},
ISSN={1083-4427},
key_old = {4390004},
 pdf = {das_abraham_konar_gaclusteringlabelkvar_acde_2008.pdf},
}
          

@article{Bandyopadhuay:Maulik:GAclustering:MO:2007,
  author    = {Bandyopadhuay, S. and Maulik, U.},
  title     = {Multiobjective genetic clustering for pixel classification in remote sensing imagery},
  journal   = {IEEE Trans. Geosci. Remote Sensing},
  volume    = {45},
  issue     = {5},
  month     = {May},
  number    = {},
  year      = {2007},
  issn      = {0196-2892},
  pages     = {1506--1511},
  ee        = {},
  bibsource = {},
  file      = {2007_bandyopadhyay_maulik_gaclustering_multiobjetive_indexi.pdf},
  key_old = {},
}

@book{Kaufman:Rousseeuw:Book:ClusterAnalysis:1990,
  added-at = {2012-08-28T06:59:18.000+0200},
  address = {New York},
  author = {Kaufman, L. and Rousseeuw, P. J.},
  biburl = {http://www.bibsonomy.org/bibtex/25873cc08151ea18ae8cf9569f4f9bf34/weeeee},
  description = {CCNLab BibTeX},
  interhash = {119bf8c432712ad3bbc1612759e0b7b4},
  intrahash = {5873cc08151ea18ae8cf9569f4f9bf34},
  keywords = {myown},
  publisher = {John Wiley and Sons},
  timestamp = {2012-08-28T06:59:18.000+0200},
  title = {Finding groups in data: an introduction to cluster analysis},
  year = {1990},
  key_old = {KaufmanRousseeuw90},
  pdf = {kaufmanR90.pdf},
}

@article{Jain:Murty:Flynn:ClusteringSurvey:1999,
 author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
 title = {Data Clustering: A Review},
 journal = {ACM Comput. Surv.},
 issue_date = {Sept. 1999},
 volume = {31},
 number = {3},
 month = sep,
 year = {1999},
 issn = {0360-0300},
 pages = {264--323},
 numpages = {60},
 url = {http://doi.acm.org/10.1145/331499.331504},
 doi = {10.1145/331499.331504},
 acmid = {331504},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {cluster analysis, clustering applications, exploratory data analysis, incremental clustering, similarity indices, unsupervised learning},
 key_old = {ain:1999:DCR:331499.331504},
 file = {1999_jain_murty_flynn_clusteringsurvey.pdf},
}

@article{milligan:cooper:clusteranalysis:1988,
 author = {Milligan, G.W. and Cooper, M.C.},
 title = {A study of standardization of variables in cluster analysis},
 journal = {J. Classification},
 volume = {5},
 number = {},
 year = {1988},
 issn = {},
 pages = {181--204},
 doi = {http://www.springerlink.com/content/t588424722r23031/},
 publisher = {},
 file  = {1988_milligan_cooper_clusteranalysis_stdvar.pdf},
 address = {},
}

@InProceedings{Zhao:Xu:Franti:ClusterMeasure:WBIndex:2009,
author={Zhao, Qinpei
and Xu, Mantao
and Fr{\"a}nti, Pasi},
editor={Kolehmainen, Mikko
and Toivanen, Pekka
and Beliczynski, Bartlomiej},
title={Sum-of-Squares Based Cluster Validity Index and Significance Analysis},
booktitle={Adaptive and Natural Computing Algorithms},
year={2009},
publisher={Springer Berlin Heidelberg},
address={Berlin, Heidelberg},
pages={313--322},
abstract={Different clustering algorithms achieve different results with certain data sets because most clustering algorithms are sensitive to the input parameters and the structure of data sets. The way of evaluating the result of the clustering algorithms, cluster validity, is one of the problems in cluster analysis. In this paper, we build a framework for cluster validity process, while proposing a sum-of-squares based index for purpose of cluster validity. We use the resampling method in the framework to analyze the stability of the clustering algorithm, and the certainty of the cluster validity index. For homogeneous data based on independent variables, the proposed clustering validity index is effective in comparison to some other commonly used indexes.},
isbn={978-3-642-04921-7},
key_old={10.1007/978-3-642-04921-7_32},
pdf={},
}

@article{Zhao:Franti:ClusterMeasure:WBIndex:2014,
title = {WB-index: A sum-of-squares based index for cluster validity},
journal = {Data & Knowledge Engineering},
volume = {92},
pages = {77 - 89},
year = {2014},
issn = {0169-023X},
doi = {https://doi.org/10.1016/j.datak.2014.07.008},
url = {http://www.sciencedirect.com/science/article/pii/S0169023X14000676},
author = {Qinpei Zhao and Pasi Fränti},
keywords = {Cluster validity, Keywords categorization, Short text mining, Clustering, Classification and association rules},
abstract = {Determining the number of clusters is an important part of cluster validity that has been widely studied in cluster analysis. Sum-of-squares based indices show promising properties in terms of determining the number of clusters. However, knee point detection is often required because most indices show monotonicity with increasing number of clusters. Therefore, indices with a clear minimum or maximum value are preferred. The aim of this paper is to revisit a sum-of-squares based index called the WB-index that has a minimum value as the determined number of clusters. We shed light on the relation between the WB-index and two popular indices which are the Calinski–Harabasz and the Xu-index. According to a theoretical comparison, the Calinski–Harabasz index is shown to be affected by the data size and level of data overlap. The Xu-index is close to the WB-index theoretically, however, it does not work well when the dimension of the data is greater than two. Here, we conduct a more thorough comparison of 12 internal indices and provide a summary of the experimental performance of different indices. Furthermore, we introduce the sum-of-squares based indices into automatic keyword categorization, where the indices are specially defined for determining the number of clusters.}
key_old={ZHAO201477},
pdf={2014_zhao_franti_clustermeasure_wbindex.pdf},
}
